Identifying correlated roles using a system driven by a neural network

ABSTRACT

A device receives a request associated with standardizing organization-specific roles within an organization, where the request includes data that identifies titles for the organization-specific roles. The device converts the data to vectors that represent semantic meanings of the titles. The device sets a configuration of a data model by assigning weighted values to title-class identifiers that are used to associate titles, of a standardized set of titles, to a hierarchy of role classifications. The device uses the data model to determine scores that indicate likelihoods of the titles mapping to the title-class identifiers. The device identifies, based on scores, a subset of title-class identifiers that associate particular titles, of the standardized set of titles, and particular role classifications. The subset of title-class identifiers is stored in association with information relating to the particular titles. The device performs an action based on the information relating to the particular titles.

BACKGROUND

Machine learning is a field of computer science that gives computers theability to learn without being explicitly programmed. For example, amachine learning model may be trained on a set of training data, suchthat the model may be used to process live data to generate usefulpredictions and/or classifications.

SUMMARY

According to some implementations, a method may include receiving, by adevice, a request associated with standardizing a set oforganization-specific roles within an organization. The request mayinclude information comprising one or more of: data that identifies aset of titles for the set of organization-specific roles, or an industryidentifier of an industry in which the organization operates. The methodmay include converting, by the device, the data that identifies the setof titles to a set of vectors that represent semantic meanings of theset of titles. The method may include setting, by the device and basedon the information included in the request, a configuration of a datamodel that is capable of scoring the set of titles. Setting theconfiguration may include assigning weighted values to a set oftitle-class identifiers that are used to associate a standardized set oftitles to a hierarchy of role classifications that identify types of thestandardized roles within organizations. The weighted values may beassigned based on at least one of: a specific hierarchy of roleclassifications within the organization, or the industry in which theorganization operates. The method may include determining, by the deviceand by using the data model that has been configured with the assignedweighted values to process the set of vectors, a set of scores thatindicate likelihoods of the set of titles mapping to the set oftitle-class identifiers. The method may include identifying, by thedevice and based on the set of scores, a subset of title-classidentifiers, of the set of title-class identifiers, that associateparticular titles, of the standardized set of titles, and particularrole classifications that are part of the hierarchy of roleclassifications. The subset of title-class identifiers may be stored inassociation with information relating to particular standardized roles.The method may include performing, by the device and by using theinformation relating to the particular standardized roles, one or moreactions to cause one or more tasks associated with the set oforganization-specific roles to be modified or eliminated.

According to some implementations, a device may include one or morememories, and one or more processors, operatively coupled to the one ormore memories, to receive a data model that has been trained to maporganization-specific roles to a standardized set of titles forstandardized roles within organizations, and a hierarchy of roleclassifications that identify classes used to group the standardizedroles. The one or more processors may receive a request associated withstandardizing a set of organization-specific roles within anorganization. The request may include information comprising data thatidentifies a set of titles for the set of organization-specific roles.The one or more processors may convert the data that identifies the setof titles to a set of vectors that represent semantic meanings of theset of titles, and may determine, by using the data model to process theset of vectors, a set of scores that indicate likelihoods of the set oftitles mapping to a set of title-class identifiers. The set oftitle-class identifiers may be used to associate the standardized set oftitles to the hierarchy of role classifications that identify types ofthe standardized roles within the organizations. The set of scores maybe determined using a cost function of the data model that assignsweighted values to the set of title-class identifiers based on theinformation included in the request. The one or more processors mayidentify, based on the set of scores, a subset of title-classidentifiers, of the set of title-class identifiers, that associateparticular titles, of the standardized set of titles, and particularrole classifications that are part of a standardized hierarchy of roleclassifications. The one or more processors may obtain informationrelating to particular standardized roles that is stored in associationwith the subset of title-class identifiers. The one or more processorsmay perform one or more actions to cause one or more tasks associatedwith the set of organization-specific roles to be modified or eliminatedbased on the information relating to the particular standardized roles.

According to some implementations, a non-transitory computer-readablemedium may store instructions that include one or more instructionsthat, when executed by one or more processors of a device, cause the oneor more processors to receive a request associated with standardizing aset of organization-specific roles within an organization. The requestmay include information comprising: data that identifies a set of titlesfor the set of organization-specific roles, and an industry identifierof an industry in which the organization operates. The one or moreinstructions may cause the one or more processors to convert the datathat identifies the set of titles to a set of vectors that representsemantic meanings of the set of titles, and may determine, by using adata model to process the set of vectors, a set of scores that indicatelikelihoods of the set of titles mapping to a set of title-classidentifiers. The set of title-class identifiers may be used to associatea standardized set of titles to a hierarchy of role classifications thatidentify classes used to group standardized roles within organizations.The set of scores may be determined using a cost function of the datamodel that assigns weighted values to the set of title-class identifiersbased on the information included in the request. The one or moreinstructions may cause the one or more processors to identify, based onthe set of scores, a subset of title-class identifiers, of the set oftitle-class identifiers, that associate particular titles, of thestandardized set of titles and particular role classifications that arepart of a standardized hierarchy of role classifications. The one ormore instructions may cause the one or more processors to obtaininformation relating to particular standardized roles that is stored inassociation with the subset of title-class identifiers. The one or moreinstructions may cause the one or more processors to perform one or moreactions to cause one or more tasks associated with the set oforganization-specific roles to be modified or eliminated based on theinformation relating to the particular standardized roles.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A-1F are diagrams of one or more example implementationsdescribed herein.

FIG. 2 is a diagram of an example environment in which systems and/ormethods described herein may be implemented.

FIG. 3 is a diagram of example components of one or more devices of FIG.2 .

FIGS. 4-6 are flow charts of an example process for mapping a set oforganization-specific roles within an organization to a set ofstandardized roles and causing one or more tasks associated with the setof organization-specific roles to be modified or eliminated by utilizinginformation made available from the mapping.

DETAILED DESCRIPTION

The following detailed description of example implementations refers tothe accompanying drawings. The same reference numbers in differentdrawings may identify the same or similar elements.

Business process improvement (BPI) is a technical field whereby anorganization may improve efficiencies of one or more organizationalprocesses. For example, an organization may have a set of roles that areused to allocate specific tasks or functions to employees of theorganization. In this case, the organization may consider using one ormore BPI services or related services to improve efficiencies within theorganization.

One solution may involve utilizing a taxonomy of information that isprovided by a third-party organization. For example, the third-partyorganization may publish a taxonomy of information that includes a listof standardized roles (e.g., that have been standardized across one ormore industries) and may associate identifiers for the standardizedroles with information that may be used to improve a process of a roleor a task, automate a role or a task, and/or the like.

However, the organization may be unable to take advantage of the usefulinformation provided by the taxonomy because the roles within theorganization may not map or correlate directly to the standardizedroles. Furthermore, conventional natural language processing may be anineffective solution given that many roles may have similar titlesdespite being related to very different tasks. For example, the taxonomyof information may have a first standardized role titled Client ServicesAdministrator, a second standardized role titled Client ServicesCoordinator, and a third standardized role titled Client ServicesRepresentative. In this case, if the organization has a role titledClient Services Specialist, conventional natural language processing maybe ineffective at identifying which standardized role should map to therole within the organization.

Additionally, without being able to utilize the information included inthe taxonomy, the organization will be unable to optimize roles and/ortasks, which may cause devices associated with the organization to wasteresources by continuing to inefficiently carry out roles or tasks (e.g.,processing resources, network resources, memory resources, and/or thelike). For example, the organization may include a first example rolethat includes ten example organizational tasks. In this case, one ormore devices associated with the organization (e.g., a telephone, adesktop computer, a network device, a server platform, and/or the like)may expend resources to assist employees in carrying out the tasks. Ifthe information included in the taxonomy allows one or more of the tenexample organizational tasks to be automated, or identifies a processimprovement that eliminates a need for one or more of the exampleorganizational tasks, then the resources expended by the one or moredevices associated with the organization are wasted by assisting incarrying out tasks that would not otherwise be necessary if theinformation included in the taxonomy was available to the organization.

While this is provided by way of example, it is to be understood thatthe organization may, for example, be a large organization withthousands of employees, tens of thousands of employees, or more, andthat thousands of devices, tens of thousands of devices, or more, mayneedlessly waste resources to perform roles and/or tasks that might beeliminated or improved if the organization was unable to access theinformation included in the taxonomy.

Some implementations described herein provide a role management platformto map organization-specific roles within an organization tostandardized roles and to cause one or more tasks associated with theorganization-specific roles to be modified or eliminated based oninformation made available from the mapping. For example, an individualworking for the organization may interact with a user device to input arequest to map a set of titles for the organization-specific roles to astandardized set of titles for the standardized roles. The request maybe provided to the role management platform and the role managementplatform may convert data that identifies the set of titles to a set ofvectors that represent semantic meanings of the set of titles.

Additionally, the role management platform may set a configuration of adata model by updating one or more target weights of a cost function ofthe data model based on the information included in the request.Furthermore, the role management platform may use the data model toprocess the set of vectors to determine a set of scores that indicatelikelihoods of the set of titles mapping to the set of title-classidentifiers. This may allow the role management platform to identify,based on the set of scores, a subset of title-class identifiers thatassociate particular titles, of the standardized set of titles, andparticular role classifications that are part of the hierarchy of roleclassifications. The subset of title-class identifiers may be stored inassociation with information relating to the particular titles (as willbe explained further herein). In this case, the role management platformmay use the information relating to the particular titles to perform oneor more actions that cause tasks associated with theorganization-specific roles to be modified or eliminated.

By mapping the set of titles for the organization-specific roles to thestandardized set of titles for the standardized roles, the rolemanagement platform is able to perform actions that improve efficiencieswithin the organization. In this way, the role management platformprovides for an efficient and effective utilization of resources ofdevices associated with the organization (e.g., processing resources,network resources, memory resources, and/or the like). For example, bymapping a title of an organization-specific role to a title of astandardized role, the role management platform is able to obtaininformation relating to the standardized role, such as information thatindicates how to modify or improve one or more tasks of the standardizedrole, information that indicates how a process may be modified such thata task of the standardized role may be removed, and/or the like. Byaccessing this information, the role management platform is able toperform actions that cause the organization-specific role to beimproved, such as by eliminating an unnecessary task of theorganization-specific role, modifying a task of theorganization-specific role, and/or the like. This conserves resources ofdevices that would have otherwise inefficiently performed these taskshad the organization been unable to utilize the information associatedwith the standardized role.

FIGS. 1A-1F are diagrams of one or more example implementations 100described herein. For example, example implementation 100 may include afirst group of data storage devices (shown as Data Storage Device 1through Data Storage Device N), a second group of data storage devices(shown as Data Storage Device 1 through Data Storage Device M), a rolemanagement platform, and a client device.

As shown in FIGS. 1A-1C, the role management platform may train one ormore data models to determine likelihoods of particularorganization-specific roles mapping to particular standardized roles. Asshown in FIGS. 1D-1F, the role management platform may use the one ormore data models to map a set of titles for organization-specific roleswithin an organization to a standardized set of titles for standardizedroles and may use information that is made accessible by the mapping toperform one or more actions that cause one or more tasks associated withthe organization-specific roles to be modified or eliminated.

As shown in FIG. 1A, and by reference number 105, the role managementplatform may obtain standardized roles data from the first group of datastorage devices. For example, the role management platform may use anapplication programming interface (API) or a similar type of interfaceto obtain the standardized roles data from the first group of datastorage devices.

The standardized roles data may include data that identifies a set oftitles for a set of standardized roles, data that identifies a hierarchyof classes used to group standardized roles within organizations, a setof title-class identifiers that associate particular standardized rolesand particular classes in the hierarchy of roles, and/or the like. Thedata that identifies the set of titles may include data that identifiesor describes a name of a title for a standardized role, data thatidentifies or describes one or more tasks that are assigned to thestandardized role, and/or the like. To provide a few specific examples,the standardized set of titles may include a first subset of titles forstandardized roles within a marketing department, a second subset oftitles for standardized roles within an accounting department, a thirdsubset of titles for standardized roles within a sales department, afourth subset of titles for standardized roles within a supply chaindepartment, a fifth subset of titles for standardized roles within acustomer service department, and/or the like.

The data that identifies the hierarchy of roles may include a class typeidentifier that identifies a type of class within the hierarchy, a classidentifier that identifies a particular class within the hierarchy,and/or the like. For example, the hierarchy of roles may include classesthat are segmented based on generic groupings (e.g., a major class, aminor class, etc.), classes that are segmented based on clusters ofroles within organizations (e.g., as divided by departments or usingother means), classes that are segmented based on the types of tasksperformed within roles, classes that are segmented based on pay levelsassociated with roles, and/or the like. The classes may include, forexample, a management class, a legal class, an office and administrativesupport class, a financial operations class, a marketing and salesclass, and/or the like.

In some implementations, the classes may be further segmented into tiersof classes. For example, if the hierarchy includes two tiers of classes,a major tier and a minor tier, a management class may be defined asbeing part of a major tier class, and classes that fit within themanagement class may be defined as being part of a minor tier class(e.g., a first minor class for human resources management, a secondminor class for supply chain management, etc., or a first minor classfor mid-level management, a second minor class for high-levelmanagement, etc.).

The title-class identifiers may include a 1+N tuple of values that areused to associate a title of a standardized role with a number N ofclass tiers within the hierarchy. As shown as an example, a title-classidentifier may include a 3-tuple of values (shown as 1.00-108-13), wherea first value is a title identifier (shown as 1.00), a second value is aminor tier identifier (shown as 108), and a third value is a minor tieridentifier (shown as 13). This may allow a standardized role to, in somecases, be associated with multiple minor tiers and multiple major tiers.As an example, the standardized role may be used across multipledepartments within an organization, across multiple industries, and/orthe like. In this example, when the standardized role is performed in afirst industry, the standardized role may be associated with a firstminor class tier and a first major class tier. When the standardizedrole is performed in a second industry, the standardized role may beassociated with a second minor class tier and a second major class tier.

In some implementations, the role management platform may obtainstandardized roles data from a single data storage device (e.g., datastorage device 1, data storage device 2, etc.). In some implementations,the role management platform may obtain standardized roles data frommultiple data storage devices (e.g., two or more of the first group ofdata storage devices). For example, different third-party organizationsmay offer publicly accessible standardized roles data, and the rolemanagement platform may perform API calls to obtain the standardizedroles data from the multiple data storage devices. In this case, therole management platform may aggregate the standardized roles dataobtained from the multiple data storage devices (e.g., using one or moretechniques described further herein). In some cases, because eachthird-party organization may use different title-class identifiers, therole management platform may generate a unique title-class identifierthat corresponds to title-class identifiers obtained from eachrespective data storage device.

As shown by reference number 110, the role management platform mayobtain historical roles data that identifies a set of historical titlesfor organization-specific roles of a group of organizations. Forexample, the role management platform may obtain historical roles datausing an API or a similar type of interface. The historical roles data(and the standardized roles data) may be obtained to allow the rolemanagement platform to train a data model that is able to maporganization-specific roles to standardized roles, as described furtherherein. The historical roles data may include historical data thatidentifies a set of historical titles for organization-specific roleswithin particular organizations, data that identifies a hierarchy ofroles within the particular organizations, data that identifies anindustry in which the particular organizations operate, data thatidentifies a geographic location where one or more historical roles areperformed, and/or the like.

In this way, the role management platform obtains standardized rolesdata and historical roles data that may be used to train a data model.

As shown in FIG. 1B, the role management platform may perform one ormore actions to generate additional training data that may be used totrain the data model. For example, and as shown by reference number 115,the role management platform may associate the set of title-classidentifiers and the set of historical titles. In this case, the set ofhistorical titles may identify historical titles fororganization-specific roles within particular organizations, and therole management platform may perform one or more data aggregationtechniques, one or more natural language processing techniques, one ormore mapping techniques, and/or the like, to associate each historicaltitle with a title-class identifier of the set of title-classidentifiers.

In some implementations, the role management platform may associate theset of title-class identifiers and the set of historical titles usingone or more natural language processing techniques and/or one or moremapping techniques. For example, an organization, of the group oforganizations, may have organization-specific roles and/or a hierarchyof roles that is different than the standardized roles and hierarchyincluded in the standardized roles data. In this case, the rolemanagement platform may perform a natural language processing techniqueto identify a semantic meaning of the historical title of theorganization-specific role and a semantic meaning of a title of astandardized role. Additionally, the role management platform mayperform a mapping technique to map particular historical titles toparticular title-class identifiers. For example, the role managementplatform may perform the mapping technique to map the historical titleto the title of the standardized role based on a semantic similaritybetween the titles, based on a semantic similarity between a class tierincluded in a hierarchy of organization-specific roles and a class tierincluded in a hierarchy of standardized roles, and/or the like.

Additionally, or alternatively, one or more historical titles may havebeen previously stored in association with the set of title-classidentifiers (e.g., one or more organizations, of the group oforganizations, may have already manually generated these associations).In this case, the role management platform may perform one or more dataaggregation techniques to aggregate the set of title-class identifiersassociated with the titles for the standardized roles and thetitle-class identifiers associated with the one or more historicaltitles.

As shown by reference number 120, the role management platform maygenerate additional title-class identifiers using a system ofadversarial networks. For example, the role management platform maygenerate additional title-class identifiers using generative adversarialnetworks (GANs) and/or a similar system of adversarial networks. In thiscase, the role management platform may train GANs (or may receivetrained GANs) and may use the GANs to generate additional titles, of thestandardized set of titles, to generate additional historical titles, togenerate additional title-class identifiers for the additional titlesand for the additional historical titles, and to update the set oftitle-class identifiers to include the additional title-classidentifiers, as each described below.

In some implementations, the role management platform may train a set ofcycle GANs. For example, the role management platform may train a firstset of neural networks (referred as herein as generator networks) andmay train a second set of neural networks (referred to as herein asdiscriminator networks). In this case, the generator networks may beresponsible for generating new data that is similar to existing data(e.g., new titles for roles that are similar to titles for standardizedroles and that are similar to historical titles fororganization-specific roles). Additionally, the discriminator networksmay be responsible for analyzing initial input data and the new data todetermine a final dataset, as further described below.

In some implementations, the role management platform may use a firstgenerator network to generate additional titles for the set ofstandardized roles. For example, the role management platform mayprovide the data that identifies the standardized set of titles for theset of standardized roles as input to the first generator network tocause the first generator network to output new data that identifies theadditional titles. In some cases, the role management platform mayconvert the data to a set of vectors that represent semantic meanings ofthe set of titles (as further described elsewhere herein) and mayprovide the set of vectors as input to the first generator network.

Additionally, the role management platform may use a first discriminatornetwork to determine a final set of titles. For example, the firstdiscriminator network may have been trained using the standardized setof titles for the standardized roles, and the role management platformmay provide the new title data that identifies the additional titles (ora first set of vectors) as input to the first discriminator network tocause the first discriminator network to generate output values thatindicate likelihoods of the additional titles being permissibleextensions to the standardized set of titles for the standardized roles(e.g., based on whether an additional title satisfies a threshold levelof similarity with one or more titles for standardized roles). In thiscase, the first discriminator network may provide the output values backto the first generator network to allow the first generator network touse the output values when continuing to generate additional titles.This process may be iteratively repeated until a stop condition issatisfied, which may cause the role management platform to update thestandardized set of titles to include one or more of the additionaltitles.

In some implementations, the role management platform may use a secondgenerator network to generate additional historical titles for the setof organization-specific roles. For example, the role managementplatform may provide the historical data that identifies the set ofhistorical titles as input to the second generator network to cause thesecond generator network to output new historical data that identifiesthe additional historical titles. In some cases, the role managementplatform may convert the historical data to a set of vectors thatrepresent semantic meanings of the set of historical titles (as furtherdescribed elsewhere herein) and may provide the set of vectors as inputto the second generator network.

Additionally, the role management platform may use a seconddiscriminator network to determine a final set of historical titles. Forexample, the second discriminator network may have been trained usingthe set of historical titles, and the role management platform mayprovide the new historical data that identifies the additional titles(or a first set of vectors) as input to the second discriminator networkto cause the second discriminator network to generate output values thatindicate likelihoods of the additional historical titles beingpermissible extensions to the set of historical titles (e.g., based onwhether an additional historical title satisfies a threshold level ofsimilarity with one or more historical titles). In this case, the seconddiscriminator network may provide the output values back to the secondgenerator network to allow the second generator network to use theoutput values when continuing to generate additional historical titles.This process may be iteratively repeated until a stop condition issatisfied, which may cause the role management platform to update theset of historical titles to include one or more of the additionalhistorical titles.

In some implementations, the role management platform may use a thirdgenerator network to generate additional title-class identifiers. Forexample, the role management platform may provide the new data thatidentifies the additional titles and the new historical data thatidentifies the additional historical titles as input to the thirdgenerator network to cause the third generator network to output theadditional title-class identifiers. In some cases, the role managementplatform may convert the new data and the new historical data to a setof vectors (as further described elsewhere herein) and may provide theset of vectors as input to the third generator network.

Additionally, the role management platform may use a third discriminatornetwork to determine a final set of title-class identifiers. Forexample, the third discriminator network may have been trained using theset of title-class identifiers, and the role management platform mayprovide the additional title-class identifiers (or a first set ofvectors) as input to the third discriminator network to cause the thirddiscriminator network to generate output values that indicatelikelihoods of the additional title-class identifiers being permissibleidentifiers to include in the set of title-class identifiers (e.g.,based on whether the additional title-class identifiers are within apermissible range of identifier values that are not used by existingtitle-class identifiers). In this case, the third discriminator networkmay provide the output values back to the third generator network toallow the third generator network to use the output values whencontinuing to generate additional title-class identifiers. This processmay be iteratively repeated until a stop condition is satisfied, whichmay cause the role management platform to update the set of title-classidentifiers to include one or more of the additional title-classidentifiers.

It is to be understood that one or more of the implementations describedabove are provided by way of example. In practice, another combinationof neural networks may be used. For example, a single generator networkmay be averse to a single discriminator network, where the singlegenerator network performs the functions that are described as beingperformed by the three generator networks and the single discriminatornetwork performs the functions that are described as being performed bythe three discriminator networks.

In this way, the role management platform is able to generate additionaltitles, additional historical titles, and additional title-classidentifiers that may be used as training data to train the data modelthat is capable of mapping titles for organization-specific roles totitles for standardized roles, as further described below.

As shown in FIG. 1C, and by reference number 125, the role managementplatform may convert the standardized set of titles for the standardizedroles and the set of historical titles for the organization-specificroles within the organizations to a set of vectors. For example, therole management platform may convert the standardized set of titles tovectors that represent semantic meanings of the standardized set oftitles and may convert the set of historical titles to vectors thatrepresent semantic meanings of the set of historical titles. In thiscase, the role management platform may perform the conversion using oneor more neural networks that are trained to produce word embeddings.

As an example, the role management platform may convert the standardizedset of titles and the set of historical titles to a set of vectors usinga two-layer Word2vec neural network. In this example, the rolemanagement platform may provide the standardized set of titles and theset of historical titles as input to the two-layer Word2vec neuralnetwork to cause the two-layer Word2vec neural network to output a setof vectors. The set of vectors may be an array of numerical values thatrepresent semantic meanings of titles and historical titles. To generatethe set of vectors, the two-layer Word2vec neural network may processthe standardized set of titles and set of historical titles using acontinuous bag-of-words (CBOW) technique, a continuous skip-gramtechnique, and/or a similar type of technique.

As shown by reference number 130, the role management platform may traina data model to generate scores that indicate likelihoods of titles fororganization-specific roles mapping to particular title-classidentifiers. For example, the role management platform may train a datamodel by using one or more machine learning techniques to analyze thedata that identifies the standardized set of titles, the data thatidentifies the set of historical titles, set of vectors, and/or thelike. The one or more machine learning techniques may include one ormore classification techniques, one or more regression techniques, oneor more techniques used specifically for training a neural network(e.g., a feedforward technique, a backpropagation technique, and/or thelike), and/or the like.

In some implementations, the role management platform may train a neuralnetwork. For example, the role management platform may train a neuralnetwork that has an input layer, one or more intermediate layers (e.g.,a fully connected layer, a convolutional layer, a pooling layer, arecurrent layer, and/or the like), and an output layer. To train theneural network, the role management platform may initialize a set ofnetwork weights that may be used when processing the set of vectors, mayinitialize a set of target weights that may reflect a relevance of aclass to a particular industry or organization, may perform afeedforward technique to analyze the set of vectors to determine a firstset of scores, may perform a backpropagation technique to update the setof network weights, and may iteratively repeat these steps until a stopcondition is satisfied (e.g., until scores generated by the neuralnetwork match known output values, until a threshold level of accuracyof satisfied, and/or the like), as further described below.

In some implementations, the role management platform may initialize theset of network weights. For example, the role management platform mayinitialize the set of network weights randomly, using pre-determinedvalues, and/or the like. The set of network weights may be updated viathe backpropagation technique, as further described below.

Additionally, or alternatively, the role management platform mayinitialize a set of target weights. For example, the set of targetweights may be hyperparameters and the role management platform may usethe set of target weights as part of a cost function of the neuralnetwork. The cost function of the neural network may be used to weightparticular portions of title-class identifiers. For example, if atitle-class identifier associates a title with a number N of classes,then a target weight, of the set of target weights, may include 1+Ntarget sub-weights, where each sub-weight is assigned to a particularportion of the title-class identifier. In the example shown in FIG. 1A,the title-class identifier associates a title identifier with two classidentifiers (shown as 1.00-108-13). In this example, because there aretwo classes, the role management platform may initialize the set oftarget weights to include three sub-weights, such that a first targetsub-weight may be assigned to the title identifier, a second targetsub-weight may be assigned to an identifier of a minor class tier, and athird target sub-weight may be assigned to an identifier of a majorclass tier.

In some implementations, the role management platform may perform afeedforward technique to analyze the set of vectors to determine aninitial set of scores. For example, the role management platform mayprovide a vector that represents an organization-specific role as inputto the neural network. This may cause the neural network to generate afirst array of values that indicate likelihoods of theorganization-specific role being particular standardized roles, a secondarray of values that indicate likelihoods of the organization role beingpart of particular minor groups, and a third array of values thatindicate likelihoods of the organization-specific role being part ofparticular major class tiers. Additionally, the neural network may,based on an analysis of the first array, the second array, and the thirdarray, generate a fourth array of values that indicate likelihoods ofthe organization-specific role mapping to the set of title-classidentifiers.

In some implementations, the role management platform may perform abackpropagation technique to update the set of network weights. Forexample, the neural network may be configured with a loss function, suchas a cross entropy loss function. In this case, the role managementplatform may execute the loss function to compare the initial set ofscores to known output values (e.g., which may indicate whichtitle-class identifiers should be mapped to each organization-specificrole) to determine an error value (e.g., an absolute error value, asquared error value, and/or the like). Additionally, the role managementplatform may update the set of network weights based on the error value.

In some implementations, the role management platform may update the setof target weights using a rule. For example, the loss function may beconfigured using a rule that allows an incorrect prediction to causelarger, or smaller, error values, depending on whether the incorrectprediction has incorrectly predicted a title, incorrectly predicted afirst class tier in the hierarchy, incorrectly predicted an Nth classtier in the hierarchy, etc.

As a specific example, the loss function may generate a first errorvalue based on an incorrect prediction being made with respect to atitle, a second error value based on an incorrect prediction being madewith respect to a minor tier class, and a third error value based on anincorrect prediction being made with respect to a major tier class. Inthis example, the loss function may have been configured to generatelarger error values for higher tiers of classification within thehierarchy. For example, the first error value might be much smaller thanthe second error value and the second error value might be much smallerthan the third error value. This allows the role management platform totrain the neural network in a manner that effectively makes the neuralnetwork risk averse to making incorrect predictions that are associatedwith mappings in higher tiers of classes within the hierarchy.

As a specific example, for each input value that is mapped to an outputvalue via the data model, the role management platform may inversely mapW to {acute over (W)} in the following way: W¹→{acute over(W)}¹:[0,1]→[1, h¹]; W²→{acute over (W)}²:[0,1]→[1, h²]; and W³→{acuteover (W)}³:[0,1]→[1, h³]. In this notation, W (e.g., W¹, W², W³)represents a target weight, {acute over (W)} (e.g., {acute over (W)}¹,{acute over (W)}², {acute over (W)}³) represents an inverse of thetarget weight, and h represents a user-defined penalty that may beintegrated into a cost function of the data model in the event that thedata model misclassifies a role. Additionally, the role managementplatform may be configured with the following cross entropy lossfunction: −Σ_(n=1) ^(N)Σ_(m=1) ^(M) _(Wn) ^(˜m)(

log y_(n) ^(m)+(1−

)log(1−y_(n) ^(m))).

In this example, m may represent a particular standardized role, n mayrepresent a number of levels in a role hierarchy, and w may represent atarget weight. The first summation shown may take a sum of error valuesgenerated for each level of a role hierarchy, which may be determined bythe second summation. The second summation shown may take a sum of errorvalues that are calculated for predictions that, for a given inputvalue, are made for a group of standardized roles in the role hierarchy.These error values may, for example, be generated for each level of therole hierarchy (and summed using the first summation, as describedabove). Furthermore, the notation _(Wn) ^(˜m) may represent a weight toapply to an error value, given a particular m value and a particular nvalue. The first portion of the function (shown as

log y_(n) ^(m)) may output an error value that is based on whether apredicted value output by the data model was correct and the secondportion of the function (shown as (1−

)log(1−y_(n) ^(m))) may output an error value that is based on apredicted value being incorrect.

In some implementations, the role management platform may iterativelyperform one or more functions described above until a stop condition issatisfied. For example, the role management platform may determine anupdated set of scores, may use the loss function to determine a newerror value and may update the set of weights based on the new errorvalue. By comparing error values for each training iteration, the rolemanagement platform is able to identify an impact that variouscombinations of weights have on the neural network's ability toaccurately make predictions. As a result, the role management platformis able to continue to update the set of weights until a properdistribution of weights is identified (e.g., that causes a thresholdnumber of predictions to be made correctly, etc.).

In some implementations, the role management platform may generate oneor more configurations of different sets of weights. For example, therole management platform may determine that a particular set of weightsis more likely (or less likely) to generate accurate outputs dependingon a given set of inputs.

As an example, the role management platform may generate a firstconfiguration that includes a first set of weights. In this example, therole management platform may determine, while training the neuralnetwork, that if input data identifies a particular hierarchy of classesof an organization, then a first set of weights (e.g.,hierarchy-specific weights) is to be applied. As another example, therole management platform may generate a second configuration thatincludes a second set of weights. In this example, the role managementplatform may determine, while training the neural network, that if inputdata includes an industry identifier that identifies an industry inwhich an organization operates, then the second set of weights (e.g.,industry-specific weights) is to be applied.

As another example, the role management platform may generate a thirdconfiguration that includes a third set of weights. In this example, therole management platform may determine, while training the neuralnetwork, that if input data includes a particular geographic location,then the third set of weights (e.g., location-specific weights) is to beapplied. As another example, the role management platform may generate adefault configuration that includes a default set of weights. Thisconfiguration may be used when none of the other configurations isapplicable. It is to be understood that these configurations areprovided by way of example. In practice, the neural network may betrained such that any number of different configurations may beimplemented.

In this way, the role management platform is able to train a data modelthat has one or more configurations that may be used, depending upon thecontext of the input data that is received by the data model.

As shown in FIG. 1D, and by reference number 135, the client device mayinput a request associated with standardizing organization-specificroles within an organization. For example, a user may use the clientdevice to interact with an interface of an application or a website thatallows the user to input a request to standardize organization-specificroles. The user may want to standardize the organization-specific rolessuch that each organization-specific role maps to a standardized role,of the standardized roles described elsewhere herein. This is becausedata that identifies the standardized roles may be stored in associationwith information that may be used for optimizing particularorganization-specific roles, as described in detail herein.

In some implementations, the user may input, as part of the request,information that may be used by the role management platform to map theorganization-specific roles to the standardized roles. The informationmay include data that identifies a set of titles fororganization-specific roles that are to be mapped to the standardizedroles (or data that identifies a storage location of the set of titlesfor the organization-specific roles), an industry identifier thatidentifies an industry in which the organization operates, location datathat identifies one or more geographic locations in which particularorganization-specific roles are performed (e.g., data that identifiesone or more sets of geographic coordinates, etc.), and/or the like. Whenthe request is submitted, the request may be provided to the rolemanagement platform.

In some implementations, the user may input data that identifies the setof titles for the organization-specific roles by selecting a menu optiondisplayed via the interface. In this case, selecting the menu option mayprovide the role management platform with permission to search a datastructure for data that identifies the set of titles fororganization-specific roles. In some implementations, the user mayupload data that identifies the set of titles for theorganization-specific roles. For example, the user may have a documentthat includes the data that identifies the set of titles for theorganization-specific roles and may upload the document as part of therequest. In some implementations, the user may input a link to a storagelocation that indicates where the data that identifies the set of titlesfor the organization-specific roles are stored. In this case, the rolemanagement platform may use the link to obtain the data that identifiesthe set of titles for the organization-specific roles.

As shown by reference number 140, the role management platform mayconvert the data that identifies the set of titles to a set of vectorsthat represent semantic meanings of the set of titles. For example, therole management platform may convert the data that identifies the set oftitles to a set of vectors using a two-layer neural network (e.g., aWord2vec neural network, etc.) or a similar type of network ortechnique. The two-layer neural network may have been trained to mapspecific strings of text to a vector space (e.g., using a continuousbag-of-words (CBOW) technique, a continuous skip-gram technique, and/orthe like). In this case, the role management platform may provide thedata that identifies the set of titles as input to the two-layer neuralnetwork to cause the two-layer neural network to output, for each title,of the set of titles, a vector (e.g., an array of values) thatrepresents a semantic meaning of a title of a role.

As shown by reference number 145, the role management platform may set aconfiguration of the data model. For example, if the data model is aneural network, the role management platform may set a configuration ofthe neural network by updating the set of weights of the neuralnetwork's cost function (e.g., based on the information included in therequest). This is because the role management platform may, whiletraining the neural network, have determined that differentconfigurations of weights are more likely to generate accurate outputsgiven particular inputs.

As an example, if the request includes the data that identifies the setof titles for the organization-specific roles within the organization(but no additional data), the role management platform may select afirst set of weights to use for the cost function of the neural network.However, if the request includes data that identifies an industry inwhich the organization operates, then the role management platform mayselect a second set of weights to use for the cost function. This isbecause a particular organization-specific role may exist acrossmultiple industries but may map to a different standardized role and/ora different title-class identifier based on an industry in which theparticular organization-specific role is being performed.

Additionally, or alternatively, and provided as another example, if therequest includes data that identifies a geographic location in which oneor more organizational-specific roles are being actively performed, thenthe role management platform may select a third set of weights to usefor the cost function. This is because cultural norms, which in somecases may be common (and thus identifiable) across certain geographiclocations, may assign different tasks and/or responsibilities to aparticular organization-specific role, which may cause the particularorganization-specific role to map to a different standardized roleand/or a different title-class identifier based on the geographiclocation in which the particular organization-specific role is beingperformed.

It is to be understood that these configurations are provided by way ofexample. In practice, any combination of these configurations may beimplemented and/or other configurations may be implemented that are notexplicitly described herein. Furthermore, while setting a configurationof the data model is described as a pre-step to using the data model togenerate scores, it is to be understood that in some implementations,processing needed to set the configuration may be performed as part ofthe data model's processing (e.g., as part of the processing performedin connection with reference number 150, as described below).

In this way, the role management platform is able to set theconfiguration of the data model based on the information included in therequest.

As shown in FIG. 1E, and by reference number 150, the role managementplatform may use the data model to determine a set of scores thatindicate likelihoods of the set of titles mapping to the set oftitle-class identifiers. For example, the role management platform mayprovide, as input data to the neural network, data that identifies theset of titles of the organization-specific roles, the set of vectors,data that identifies an industry in which the organization operates,location data that identifies a geographic location at which one or moreorganization-specific roles are performed, and/or the like. This maycause the neural network to analyze the input data to determine a set ofscores that indicate likelihoods of the set of titles mapping to the setof title-class identifiers. Additional details regarding how the neuralnetwork processes input data may be found elsewhere herein (e.g., inFIG. 1C).

As shown by reference number 155, the role management platform mayidentify a subset of title-class identifiers, of the set of title-classidentifiers, based on the set of scores. For example, the rolemanagement platform may, for each organization-specific role within theorganization, have determined scores that indicate likelihoods of eachorganization-specific role mapping to a particular title-classidentifier. In this case, the role management platform may identify abest-available score for each organization-specific role, which may beassociated with a particular title-class identifier. As a specificexample, if there are one thousand title-class identifiers, the rolemanagement platform may, for a title of an organization-specific role,determine a score for each title-class identifier, and may identify aparticular title-class identifier that has a best-available score (e.g.,a highest score, etc.).

As shown by reference number 160, the role management platform mayobtain information relating to standardized roles using the subset oftitle-class identifiers that have been identified. For example, the rolemanagement platform may use the subset of title-class identifiers tosearch a data structure that associates the subset of title-classidentifiers with the information relating to the standardized roles.

The information relating to the standardized roles may includeinformation describing a set of tasks performed as part of thestandardized roles, information describing a set of skills, knowledge,abilities, and/or work experience needed to effectively perform thestandardized roles, information identifying relationships betweenstandardized roles and particular tasks, payment information across oneor more industries that identifies what organizations are usually payingindividuals for performing for the standardized roles, contextualinformation relating to the set of tasks, and/or the like. Thecontextual information may indicate a distribution of time spentperforming particular tasks of a standardized role. For example, thecontextual information may indicate that individuals performing aparticular standardized role often spent X amount of time working at adesktop computer, Y amount of time working with groups or teams, Zamount of time checking e-mails or taking telephone calls, and/or thelike.

In this way, the role management platform is able to determine a set ofscores and to use the set of scores to obtain information that may beused to improve one or more organization-specific roles within theorganization, as described further herein.

As shown in FIG. 1F, and by reference number 165, the role managementplatform may generate a recommendation to modify or eliminate one ormore tasks associated with one or more organization-specific roles. Forexample, the role management platform may generate a recommendationbased on an analysis of information relating to a correspondingstandardized role. The recommendation may include data that identifiesone or more tasks or organization-specific roles that are to be modifiedor eliminated, data that identifies when to modify or to eliminate theone or more tasks or organization-specific roles, data that identifieshow to modify or to eliminate the one or more tasks ororganization-specific roles, and/or the like.

In some implementations, the role management platform may generate arecommendation to modify or eliminate a task of an organization-specificrole based on an analysis of information relating to a correspondingstandardized role. For example, the role management platform may analyzethe information that relates to the corresponding standardized role (asdefined elsewhere herein) and may identify a set of discrepanciesbetween the standardized role and the organization-specific role. Inthis case, the role management platform might identify tasks performedas part of the organization-specific role that are not performed as partof the standardized role (or vice versa), might identify skills,knowledge, abilities, and/or work experience that were thought to berequired for the organization-specific role but that were not requiredfor the standardized role (or vice versa), might identify that theorganization-specific role involves spending a disproportionate amountof time on a particular task relative to the standardized role (or viceversa), and/or the like. Furthermore, in some cases, the role managementplatform might determine that multiple organization-specific roles aredescribed as a single standardized role. Additionally, the rolemanagement platform may analyze the set of discrepancies to generate arecommendation to modify or remove one or more tasks of theorganization-specific role (e.g., such that the organization-specificrole is standardized to become more similar to the standardized role,etc.).

As shown by reference number 170, the role management platform mayprovide mapping data and/or the recommendation to the client device. Themapping data may identify associations between the organization-specificroles within the organization and the standardized roles. In this case,the role management platform may provide the mapping data and/or therecommendation to the client device via an interface, such as an APIassociated with the application or a website used to create the request,and/or via a similar type of interface.

As shown by reference number 175, the client device may display themapping data and/or the recommendation via the interface. This may allowa user associated with the organization to view the mapping data and/orthe recommendation, and may allow the user to determine whether toimplement the recommendation, whether to provide feedback informationthat indicates an accuracy of content included within the mapping data,whether to provide feedback information to modify the recommendation,and/or the like.

In some implementations, the client device may generate and provide therole management platform with feedback information that indicates anaccuracy of the content included within the mapping data. For example,the client device may notice that certain mappings betweenorganization-specific roles and standardized roles are not accurate. Inthis case, the client device may provide feedback information to therole management platform. This may allow the role management platform toretrain the data model. For example, the role management platform mayupdate the set of weights that are used as part of the cost function,such that the data model may be able to accurately score subsequentmappings that are requested by the client device.

In some implementations, the client device may generate and provide therole management platform with feedback information that indicates tomodify the recommendation. For example, a user may agree with a firstpart of the recommendation but disagree with a second part of therecommendation. In this case, the user may input feedback informationthat indicates to modify the second part of the recommendation and mayprovide the feedback information to the role management platform. Thismay allow the role management platform to analyze the feedbackinformation in a manner that improves accuracy of subsequentrecommendations.

In some implementations, the role management platform may perform one ormore actions that cause the recommendation to be implemented. Forexample, the role management platform may, as described above, providethe recommendation to the client device, which may cause the clientdevice to perform one or more actions that cause the recommendation tobe implemented. Additionally, or alternatively, the role managementplatform may assist the organization in implementing the recommendation.For example, if a task is recommended to be modified or removed, therole management platform may generate an updated list of tasks for anorganization-specific role, such that the list identifies and/ordescribes the task that has been modified (or that omits the task thathas been removed). In this case, the role management platform may alsoprovide a notification to devices associated with one or more employeesthat may be responsible for performing the modified tasks (or no longerperforming the removed task). Additionally, or alternatively, andprovided as another example, if a new task is recommended to be added tothe organization-specific role, the role management platform maygenerate instructions on how to perform the new task, may notify devicesof one or more employees that will be responsible for performing the newtask, and/or the like.

By mapping the organization-specific roles to the standardized roles,the role management is able to perform actions that improve efficiencieswithin the organization. In this way, the role management platformprovides for an efficient and effective utilization of resources ofdevices associated with the organization (e.g., processing resources,network resources, memory resources, and/or the like).

As indicated above, FIGS. 1A-1F are provided merely as one or moreexamples. Other examples may differ from what is described with regardto FIGS. 1A-1F. For example, there may be additional devices and/ornetworks, fewer devices and/or networks, different devices and/ornetworks, or differently arranged devices and/or networks than thoseshown in FIGS. 1A-1F. Furthermore, two or more devices shown in FIGS.1A-1F may be implemented within a single device, or a single deviceshown in FIGS. 1A-1F may be implemented as multiple and/or distributeddevices. Additionally, or alternatively, a set of devices (e.g., one ormore devices) of example implementation 100 may perform one or morefunctions described as being performed by another set of devices ofexample implementation 100.

FIG. 2 is a diagram of an example environment 200 in which systemsand/or methods described herein may be implemented. As shown in FIG. 2 ,environment 200 may include a client device 210, a data storage device220, a role management platform 230 hosted within a cloud computingenvironment 240, and/or a network 250. Devices of environment 200 mayinterconnect via wired connections, wireless connections, or acombination of wired and wireless connections.

Client device 210 includes one or more devices capable of receiving,generating, storing, processing, and/or providing information associatedwith roles within an organization. For example, client device 210 mayinclude a communication and/or computing device, such as a mobile phone(e.g., a smart phone, a radiotelephone, etc.), a laptop computer, atablet computer, a handheld computer, a server computer, a gamingdevice, a wearable communication device (e.g., a smart wristwatch, apair of smart eyeglasses, etc.), or a similar type of device. In someimplementations, client device 210 may provide, to role managementplatform 230, a request associated with standardizing a set oforganization-specific roles within an organization. In someimplementations, client device 210 may receive, from role managementplatform 230, information that may be used to satisfy the request. Forexample, client device 210 may receive data identifying title-classidentifiers and/or a recommendation, as further described elsewhereherein.

Data storage device 220 includes one or more devices capable ofreceiving, storing, generating, determining, and/or providinginformation associated with organization-specific roles within anorganization and/or standardized roles within an industry ortaxonomy-defined domain. For example, data storage device 220 mayinclude a server device or a group of server devices. In someimplementations, data storage device 220 may use one or more datastructures to store data, such as a database (e.g., a relationaldatabase), a linked-list, an array, a tree, a graph, a taxonomy, and/ora similar type of data structure. In some implementations, a first datastorage device 220 may provide, to role management platform 230, datathat identifies a set of titles for standardized roles (e.g., which maybe standardized as part of a taxonomy) and data identifying a hierarchyof role classifications. Additionally, or alternatively, a second datastorage device 220 may provide data that identifies a set of historicaltitles for organization-specific roles for a group of organizations torole management platform 230.

Role management platform 230 includes one or more devices capable ofreceiving, storing, generating, determining, and/or providinginformation associated with servicing requests provided by client device210. For example, role management platform 230 may include a serverdevice (e.g., a host server, a web server, an application server, etc.),a data center device, or a similar device. In some implementations, rolemanagement platform 230 may train a data model (e.g., a neural network,etc.) and/or may receive a trained data model. In this case, rolemanagement platform 230 may use the data model to process requests madeby client device 210.

In some implementations, as shown, role management platform 230 may behosted in cloud computing environment 240. Notably, whileimplementations described herein describe role management platform 230as being hosted in cloud computing environment 240, in someimplementations, role management platform 230 might not be cloud-based(i.e., may be implemented outside of a cloud computing environment) ormay be partially cloud-based.

Cloud computing environment 240 includes an environment that hosts rolemanagement platform 230. Cloud computing environment 240 may providecomputation, software, data access, storage, etc. services that do notrequire end-user knowledge of a physical location and configuration ofsystem(s) and/or device(s) that hosts role management platform 230. Asshown, cloud computing environment 240 may include a group of computingresources 235 (referred to collectively as “computing resources 235” andindividually as “computing resource 235”).

Computing resource 235 includes one or more personal computers,workstation computers, server devices, or another type of computationand/or communication device. In some implementations, computing resource235 may host role management platform 230. The cloud resources mayinclude compute instances executing in computing resource 235, storagedevices provided in computing resource 235, data transfer devicesprovided by computing resource 235, and/or the like. In someimplementations, computing resource 235 may communicate with othercomputing resources 235 via wired connections, wireless connections, ora combination of wired and wireless connections.

As further shown in FIG. 2 , computing resource 235 may include a groupof cloud resources, such as one or more applications (“APPs”) 235-1, oneor more virtual machines (“VMs”) 235-2, virtualized storage (“VSs”)235-3, one or more hypervisors (“HYPs”) 235-4, and/or the like.

Application 235-1 may include one or more software applications that maybe provided to or accessed by client device 210 and/or data storagedevice 220. Application 235-1 may eliminate a need to install andexecute the software applications on these devices. For example,application 235-1 may include software associated with role managementplatform 230 and/or any other software capable of being provided viacloud computing environment 240. In some implementations, oneapplication 235-1 may send/receive information to/from one or more otherapplications 235-1, via virtual machine 235-2. In some implementations,application 235-1 may include one or more functions and/or features thatallow client device 210 to generate requests, as further describedelsewhere herein.

Virtual machine 235-2 may include a software implementation of a machine(e.g., a computer) that executes programs like a physical machine.Virtual machine 235-2 may be either a system virtual machine or aprocess virtual machine, depending upon use and degree of correspondenceto any real machine by virtual machine 235-2. A system virtual machinemay provide a complete system platform that supports execution of acomplete operating system (“OS”). A process virtual machine may executea single program and may support a single process. In someimplementations, virtual machine 235-2 may execute on behalf of anotherdevice (e.g., client device 210, data storage device 220, etc.), and maymanage infrastructure of cloud computing environment 240, such as datamanagement, synchronization, or long-duration data transfers.

Virtualized storage 235-3 may include one or more storage systems and/orone or more devices that use virtualization techniques within thestorage systems or devices of computing resource 235. In someimplementations, within the context of a storage system, types ofvirtualizations may include block virtualization and filevirtualization. Block virtualization may refer to abstraction (orseparation) of logical storage from physical storage so that the storagesystem may be accessed without regard to physical storage orheterogeneous structure. The separation may permit administrators of thestorage system flexibility in how the administrators manage storage forend users. File virtualization may eliminate dependencies between dataaccessed at a file level and a location where files are physicallystored. This may enable optimization of storage use, serverconsolidation, and/or performance of non-disruptive file migrations.

Hypervisor 235-4 may provide hardware virtualization techniques thatallow multiple operating systems (e.g., “guest operating systems”) toexecute concurrently on a host computer, such as computing resource 235.Hypervisor 235-4 may present a virtual operating platform to the guestoperating systems and may manage the execution of the guest operatingsystems. Multiple instances of a variety of operating systems may sharevirtualized hardware resources.

Network 250 includes one or more wired and/or wireless networks. Forexample, network 250 may include a cellular network (e.g., a fifthgeneration (5G) network, a fourth generation (4G) network, such as along-term evolution (LTE) network, a third generation (3G) network, acode division multiple access (CDMA) network, a public land mobilenetwork (PLMN), a local area network (LAN), a wide area network (WAN), ametropolitan area network (MAN), a telephone network (e.g., the PublicSwitched Telephone Network (PSTN)), a private network, an ad hocnetwork, an intranet, the Internet, a fiber optic-based network, a cloudcomputing network, or the like, and/or a combination of these or othertypes of networks.

The number and arrangement of devices and networks shown in FIG. 2 areprovided as one or more examples. In practice, there may be additionaldevices and/or networks, fewer devices and/or networks, differentdevices and/or networks, or differently arranged devices and/or networksthan those shown in FIG. 2 . Furthermore, two or more devices shown inFIG. 2 may be implemented within a single device, or a single deviceshown in FIG. 2 may be implemented as multiple, distributed devices.Additionally, or alternatively, a set of devices (e.g., one or moredevices) of environment 200 may perform one or more functions describedas being performed by another set of devices of environment 200.

FIG. 3 is a diagram of example components of a device 300. Device 300may correspond to client device 210, data storage device 220, and/orrole management platform 230. In some implementations, client device210, data storage device 220, and/or role management platform 230 mayinclude one or more devices 300 and/or one or more components of device300. As shown in FIG. 3 , device 300 may include a bus 310, a processor320, a memory 330, a storage component 340, an input component 350, anoutput component 360, and/or a communication interface 370.

Bus 310 includes a component that permits communication among multiplecomponents of device 300. Processor 320 is implemented in hardware,firmware, and/or a combination of hardware and software. Processor 320includes a central processing unit (CPU), a graphics processing unit(GPU), an accelerated processing unit (APU), a microprocessor, amicrocontroller, a digital signal processor (DSP), a field-programmablegate array (FPGA), an application-specific integrated circuit (ASIC),and/or another type of processing component. In some implementations,processor 320 includes one or more processors capable of beingprogrammed to perform a function. Memory 330 includes a random accessmemory (RAM), a read only memory (ROM), and/or another type of dynamicor static storage device (e.g., a flash memory, a magnetic memory,and/or an optical memory) that stores information and/or instructionsfor use by processor 320.

Storage component 340 stores information and/or software related to theoperation and use of device 300. For example, storage component 340 mayinclude a hard disk (e.g., a magnetic disk, an optical disk, and/or amagneto-optic disk), a solid state drive (SSD), a compact disc (CD), adigital versatile disc (DVD), a floppy disk, a cartridge, a magnetictape, and/or another type of non-transitory computer-readable medium,along with a corresponding drive.

Input component 350 includes a component that permits device 300 toreceive information, such as via user input (e.g., a touch screendisplay, a keyboard, a keypad, a mouse, a button, a switch, and/or amicrophone). Additionally, or alternatively, input component 350 mayinclude a component for determining location (e.g., a global positioningsystem (GPS) component) and/or a sensor (e.g., an accelerometer, agyroscope, an actuator, another type of positional or environmentalsensor, and/or the like). Output component 360 includes a component thatprovides output information from device 300 (via, e.g., a display, aspeaker, a haptic feedback component, an audio or visual indicator,and/or the like).

Communication interface 370 includes a transceiver-like component (e.g.,a transceiver, a separate receiver, a separate transmitter, and/or thelike) that enables device 300 to communicate with other devices, such asvia a wired connection, a wireless connection, or a combination of wiredand wireless connections. Communication interface 370 may permit device300 to receive information from another device and/or provideinformation to another device. For example, communication interface 370may include an Ethernet interface, an optical interface, a coaxialinterface, an infrared interface, a radio frequency (RF) interface, auniversal serial bus (USB) interface, a Wi-Fi interface, a cellularnetwork interface, and/or the like.

Device 300 may perform one or more processes described herein. Device300 may perform these processes based on processor 320 executingsoftware instructions stored by a non-transitory computer-readablemedium, such as memory 330 and/or storage component 340. As used herein,the term “computer-readable medium” refers to a non-transitory memorydevice. A memory device includes memory space within a single physicalstorage device or memory space spread across multiple physical storagedevices.

Software instructions may be read into memory 330 and/or storagecomponent 340 from another computer-readable medium or from anotherdevice via communication interface 370. When executed, softwareinstructions stored in memory 330 and/or storage component 340 may causeprocessor 320 to perform one or more processes described herein.Additionally, or alternatively, hardware circuitry may be used in placeof or in combination with software instructions to perform one or moreprocesses described herein. Thus, implementations described herein arenot limited to any specific combination of hardware circuitry andsoftware.

The number and arrangement of components shown in FIG. 3 are provided asan example. In practice, device 300 may include additional components,fewer components, different components, or differently arrangedcomponents than those shown in FIG. 3 . Additionally, or alternatively,a set of components (e.g., one or more components) of device 300 mayperform one or more functions described as being performed by anotherset of components of device 300.

FIG. 4 is a flow chart of an example process 400 for mapping a set oforganization-specific roles within an organization to a set ofstandardized roles and causing one or more tasks associated with the setof organization-specific roles to be modified or eliminated by utilizinginformation made available from the mapping. In some implementations,one or more process blocks of FIG. 4 may be performed by a rolemanagement platform (e.g., role management platform 230). In someimplementations, one or more process blocks of FIG. 4 may be performedby another device or a group of devices separate from or including therole management platform, such as a client device (e.g., client device210), a data storage device (e.g., data storage device 220), and/or thelike.

As shown in FIG. 4 , process 400 may include receiving a requestassociated with standardizing a set of organization-specific roleswithin an organization, wherein the request includes informationcomprising one or more of: data that identifies a set of titles for theset of organization-specific roles, or an industry identifier of anindustry in which the organization operates (block 410). For example,the role management platform (e.g., using computing resource 235,processor 320, memory 330, storage component 340, input component 350,communication interface 370, and/or the like) may receive a requestassociated with standardizing a set of organization-specific roleswithin an organization, as described above. In some implementations, therequest may include information comprising one or more of: data thatidentifies a set of titles for the set of organization-specific roles,or an industry identifier of an industry in which the organizationoperates.

As further shown in FIG. 4 , process 400 may include converting the datathat identifies the set of titles to a set of vectors that representsemantic meanings of the set of titles (block 420). For example, therole management platform (e.g., using computing resource 235, processor320, memory 330, storage component 340, and/or the like) may convert thedata that identifies the set of titles to a set of vectors thatrepresent semantic meanings of the set of titles, as described above.

As further shown in FIG. 4 , process 400 may include setting, based onthe information included in the request, a configuration of a data modelthat is capable of scoring the set of titles, wherein setting theconfiguration includes assigning weighted values to a set of title-classidentifiers that are used to associate a standardized set of titles to ahierarchy of role classifications that identify classes used to groupstandardized roles within organizations, and wherein the weighted valuesare assigned based on at least one of: a specific hierarchy of roleclassifications within the organization, or the industry in which theorganization operates (block 430). For example, the role managementplatform (e.g., using computing resource 235, processor 320, memory 330,storage component 340, and/or the like) may set, based on theinformation included in the request, a configuration of a data modelthat is capable of scoring the set of titles, as described above. Insome implementations, setting the configuration may include assigningweighted values to a set of title-class identifiers that are used toassociate a standardized set of titles to a hierarchy of roleclassifications that identify classes used to group standardized roleswithin organizations. In some implementations, the weighted values maybe assigned based on at least one of: a specific hierarchy of roleclassifications within the organization, or the industry in which theorganization operates.

As further shown in FIG. 4 , process 400 may include determining, byusing the data model that has been configured with the assigned weightedvalues to process the set of vectors, a set of scores that indicatelikelihoods of the set of titles mapping to the set of title-classidentifiers (block 440). For example, the role management platform(e.g., using computing resource 235, processor 320, memory 330, storagecomponent 340, and/or the like) may determine, by using the data modelthat has been configured with the assigned weighted values to processthe set of vectors, a set of scores that indicate likelihoods of the setof titles mapping to the set of title-class identifiers, as describedabove.

As further shown in FIG. 4 , process 400 may include identifying, basedon the set of scores, a subset of title-class identifiers, of the set oftitle-class identifiers, that associate particular titles, of thestandardized set of titles, and particular role classifications that arepart of the hierarchy of role classifications, wherein the subset oftitle-class identifiers is stored in association with informationrelating to particular standardized roles (block 450). For example, therole management platform (e.g., using computing resource 235, processor320, memory 330, storage component 340, and/or the like) may identify,based on the set of scores, a subset of title-class identifiers, of theset of title-class identifiers, that associate particular titles, of thestandardized set of titles, and particular role classifications that arepart of the hierarchy of role classifications, as described above. Insome implementations, the subset of title-class identifiers may bestored in association with information relating to particularstandardized roles.

As further shown in FIG. 4 , process 400 may include performing, usingthe relating to the particular standardized roles, one or more actionsto cause one or more tasks associated with the set oforganization-specific roles to be modified or eliminated (block 460).For example, the role management platform (e.g., using computingresource 235, processor 320, memory 330, storage component 340, inputcomponent 350, output component 360, communication interface 370, and/orthe like) may perform, using the information relating to the particularstandardized roles, one or more actions to cause one or more tasksassociated with the set of organization-specific roles to be modified oreliminated, as described above.

Process 400 may include additional implementations, such as any singleimplementation or any combination of implementations described belowand/or in connection with one or more other processes describedelsewhere herein.

In some implementations, the first weighted value and the one or moreadditional weighted values may be stored in a vector format. Forexample, the first weighted value and the one or more additionalweighted values may be stored in a vector array or matrix. This formatallows different classes or hierarchical levels of roles to beconsidered in the analysis performed by the data model, as describedelsewhere herein. In some implementations, when setting theconfiguration of the data model, the role management platform may assigna first weighted value to a title portion of a title-class identifierbased on whether a title, of the set of titles, maps to a particulartitle, of the standardized set of titles, and may assign one or moreadditional weighted values to one or more classification portions of thetitle-class identifier based on whether the title maps to one or morerole classifications of the hierarchy of role classifications.

In some implementations, the one or more additional weighted values maybe greater than the first weighted value, and the one or more additionalweighted values may be assigned based on the hierarchy of roleclassifications. In some implementations, the data model may include aneural network.

In some implementations, the role management platform may receive,before receiving the request, data that identifies a first standardizedset of titles and a first hierarchy of role classifications. In someimplementations, the role management platform may receive data thatidentifies a first set of historical titles associated with a group oforganizations. In some implementations, the role management platform maygenerate data that identifies a second standardized set of titles byusing a system of adversarial neural networks to process the data thatidentifies the first standardized set of titles and the first hierarchyof role classifications. In some implementations, the role managementplatform may generate data that identifies a second set of historicaltitles by using the system of adversarial neural networks to process thedata that identifies the first set of historical titles. In someimplementations, the role management platform may generate data thatidentifies particular title-class identifiers by using the system ofadversarial neural networks to process the data that identifies thesecond standardized set of titles and the data that identifies thesecond set of historical titles. The particular title-class identifiersmay be included in the set of title-class identifiers.

In some implementations, the role management platform, when performingthe one or more actions, may generate a recommendation to modify oreliminate a task of an organization-specific role based on an analysisof particular information that describes a corresponding standardizedrole of the particular standardized roles. Additionally, the rolemanagement platform may provide the recommendation to a user device tocause the user device to perform particular actions to implement therecommendation.

Although FIG. 4 shows example blocks of process 400, in someimplementations, process 400 may include additional blocks, fewerblocks, different blocks, or differently arranged blocks than thosedepicted in FIG. 4 . Additionally, or alternatively, two or more of theblocks of process 400 may be performed in parallel.

FIG. 5 is a flow chart of an example process 500 for mapping a set oforganization-specific roles within an organization to a set ofstandardized roles and causing one or more tasks associated with the setof organization-specific roles to be modified or eliminated by utilizinginformation made available from the mapping. In some implementations,one or more process blocks of FIG. 5 may be performed by a rolemanagement platform (e.g., role management platform 230). In someimplementations, one or more process blocks of FIG. 5 may be performedby another device or a group of devices separate from or including therole management platform, such as a client device (e.g., client device210), a data storage device (e.g., data storage device 220), and/or thelike.

As shown in FIG. 5 , process 500 may include receiving a data model thathas been trained to map titles for organization-specific roles to: astandardized set of titles for organization-specific roles withinorganizations, and a hierarchy of role classifications that identifyclasses used to group the standardized roles (block 510). For example,the role management platform (e.g., using computing resource 235,processor 320, memory 330, storage component 340, input component 350,communication interface 370, and/or the like) may receive a data modelthat has been trained to map titles to a standardized set of titles fororganization-specific roles within organizations, and to a hierarchy ofrole classifications that identify classes used to group thestandardized roles, as described above.

As further shown in FIG. 5 , process 500 may include receiving a requestassociated with standardizing a set of organization-specific roleswithin an organization, wherein the request includes informationcomprising data that identifies a set of titles for the set oforganization-specific roles (block 520). For example, the rolemanagement platform (e.g., using computing resource 235, processor 320,memory 330, storage component 340, input component 350, communicationinterface 370, and/or the like) may receive a request associated withstandardizing a set of organization-specific roles within anorganization, as described above. In some implementations, the requestmay include information comprising data that identifies a set of titlesfor the set of organization-specific roles.

As further shown in FIG. 5 , process 500 may include obtaining data thatidentifies the set of titles using the data that identifies the storagelocation (block 530). For example, the role management platform (e.g.,using computing resource 235, processor 320, memory 330, storagecomponent 340, and/or the like) may obtain data that identifies the setof titles using the data that identifies the storage location, asdescribed above.

As further shown in FIG. 5 , process 500 may include converting the datathat identifies the set of titles to a set of vectors that representsemantic meanings of the set of titles (block 540). For example, therole management platform (e.g., using computing resource 235, processor320, memory 330, storage component 340, and/or the like) may convert thedata that identifies the set of titles to a set of vectors thatrepresent semantic meanings of the set of titles, as described above.

As further shown in FIG. 5 , process 500 may include determining, byusing the data model to process the set of vectors, a set of scores thatindicate likelihoods of the set of titles mapping to a set oftitle-class identifiers, wherein the set of title-class identifiers areto be used to associate the standardized set of titles to the hierarchyof role classifications that identify classes used to group thestandardized roles within the organizations, and wherein the set ofscores are to be determined using a cost function of the data model thatassigns weighted values to the set of title-class identifiers based onthe information included in the request (block 550). For example, therole management platform (e.g., using computing resource 235, processor320, memory 330, storage component 340, and/or the like) may determine,by using the data model to process the set of vectors, a set of scoresthat indicate likelihoods of the set of titles mapping to a set oftitle-class identifiers, as described above. In some implementations,the set of title-class identifiers may be used to associate thestandardized set of titles to the hierarchy of role classifications thatidentify classes used to group the standardized roles within theorganizations. In some implementations, the set of scores may bedetermined using a cost function of the data model that assigns weightedvalues to the set of title-class identifiers based on the informationincluded in the request.

As further shown in FIG. 5 , process 500 may include identifying, basedon the set of scores, a subset of title-class identifiers, of the set oftitle-class identifiers, that associate particular titles, of thestandardized set of titles, and particular role classifications that arepart of a standardized hierarchy of role classifications (block 560).For example, the role management platform (e.g., using computingresource 235, processor 320, memory 330, storage component 340, and/orthe like) may identify, based on the set of scores, a subset oftitle-class identifiers, of the set of title-class identifiers, thatassociate particular titles, of the standardized set of titles, andparticular role classifications that are part of a standardizedhierarchy of role classifications, as described above.

As further shown in FIG. 5 , process 500 may include obtaininginformation relating to particular standardized roles that is stored inassociation with the subset of title-class identifiers (block 570). Forexample, the role management platform (e.g., using computing resource235, processor 320, memory 330, storage component 340, output component360, communication interface 370, and/or the like) may obtaininformation relating to particular standardized roles that is stored inassociation with the subset of title-class identifiers, as describedabove.

As further shown in FIG. 5 , process 500 may include performing one ormore actions to cause one or more tasks associated with the set oforganization-specific roles to be modified or eliminated based on theinformation relating to the particular standardized roles (block 580).For example, the role management platform (e.g., using computingresource 235, processor 320, memory 330, storage component 340, outputcomponent 360, communication interface 370, and/or the like) may performone or more actions to cause one or more tasks associated with the setof organization-specific roles to be modified or eliminated based on theinformation relating to the particular standardized roles, as describedabove.

Process 500 may include additional implementations, such as any singleimplementation or any combination of implementations described belowand/or in connection with one or more other processes describedelsewhere herein.

In some implementations, the request may include an industry identifierof an industry in which the organization operates, and the rolemanagement platform may set a configuration of the data model based onthe information included in the request, where setting the configurationincludes assigning the weighted values to the set of title-classidentifiers based on the industry in which the organization operates. Insome implementations, the role management platform receive, from a userdevice, feedback information indicating whether the set oforganization-specific roles map to the set of title-class identifiers.In some implementations, the one or more additional weighted values maybe greater than the first weighted value, and the one or more additionalweighted values may be assigned based on the hierarchy of roleclassifications.

In some implementations, the data model may be a neural network, and therole management platform may provide the set of vectors as input to theneural network to cause the neural network to output the set of scores.The cost function may assign each title-class identifier, of the set oftitle-class identifiers: a first weighted value to a title portion of atitle-class identifier based on whether a title, of the set of titles,maps to a title of the standardized set of titles, and one or moreadditional weighted values to one or more classification portions of thetitle-class identifier based on whether the title maps to one or morerole classifications of the hierarchy of role classifications.

In some implementations, when converting the data that identifies theset of titles, the role management platform may convert the data thatidentifies the set of titles to the set of vectors using a neuralnetwork.

In some implementations, when performing the one or more actions, therole management platform may generate a recommendation to modify oreliminate a task of an organization-specific role based on an analysisof particular information relating to a corresponding title of theparticular titles. The recommendation may include data that identifiesthe task or the organization-specific role that is to be modified oreliminated, data that identifies when to modify or eliminate the task orthe organization-specific role, data that identifies how to modify oreliminate the task or the organization-specific role, and/or the like.Additionally, the role management platform may provide therecommendation to a user device to permit the user device to display therecommendation via an interface.

Although FIG. 5 shows example blocks of process 500, in someimplementations, process 500 may include additional blocks, fewerblocks, different blocks, or differently arranged blocks than thosedepicted in FIG. 5 . Additionally, or alternatively, two or more of theblocks of process 500 may be performed in parallel.

FIG. 6 is a flow chart of an example process 600 for mapping a set oforganization-specific roles within an organization to a set ofstandardized roles and causing one or more tasks associated with the setof organization-specific roles to be modified or eliminated by utilizinginformation made available from the mapping. In some implementations,one or more process blocks of FIG. 6 may be performed by a rolemanagement platform (e.g., role management platform 230). In someimplementations, one or more process blocks of FIG. 6 may be performedby another device or a group of devices separate from or including therole management platform, such as a client device (e.g., client device210), a data storage device (e.g., data storage device 220), and/or thelike.

As shown in FIG. 6 , process 600 may include receiving a requestassociated with standardizing a set of organization-specific roleswithin an organization, wherein the request includes informationcomprising: data that identifies a set of titles for the set oforganization-specific roles, and an industry identifier of an industryin which the organization operates (block 610). For example, the rolemanagement platform (e.g., using computing resource 235, processor 320,memory 330, storage component 340, input component 350, communicationinterface 370, and/or the like) may receive a request associated withstandardizing a set of organization-specific roles within anorganization, as described above. In some implementations, the requestmay include information comprising: data that identifies a set of titlesfor the set of organization-specific roles, and an industry identifierof an industry in which the organization operates.

As further shown in FIG. 6 , process 600 may include converting the datathat identifies the set of titles to a set of vectors that representsemantic meanings of the set of titles (block 620). For example, therole management platform (e.g., using computing resource 235, processor320, memory 330, storage component 340, and/or the like) may convert thedata that identifies the set of titles to a set of vectors thatrepresent semantic meanings of the set of titles, as described above.

As further shown in FIG. 6 , process 600 may include determining, byusing a data model to process the set of vectors, a set of scores thatindicate likelihoods of the set of titles mapping to a set oftitle-class identifiers, wherein the set of title-class identifiers areto be used to associate a standardized set of titles to a hierarchy ofrole classifications that identify classes used to group standardizedroles within organizations, and wherein the set of scores are to bedetermined using a cost function of the data model that assigns weightedvalues to the set of title-class identifiers based on the informationincluded in the request (block 630). For example, the role managementplatform (e.g., using computing resource 235, processor 320, memory 330,storage component 340, and/or the like) may determine, by using a datamodel to process the set of vectors, a set of scores that indicatelikelihoods of the set of titles mapping to a set of title-classidentifiers, as described above. In some implementations, the set oftitle-class identifiers may be used to associate a standardized set oftitles to a hierarchy of role classifications that identify classes usedto group standardized roles within organizations, and the set of scoresmay be determined using a cost function of the data model that assignsweighted values to the set of title-class identifiers based on theinformation included in the request.

As further shown in FIG. 6 , process 600 may include identifying, basedon the set of scores, a subset of title-class identifiers, of the set oftitle-class identifiers, that associate particular titles, of thestandardized set of titles, and particular role classifications that arepart of a standardized hierarchy of role classifications (block 640).For example, the role management platform (e.g., using computingresource 235, processor 320, memory 330, storage component 340, and/orthe like) may identify, based on the set of scores, a subset oftitle-class identifiers, of the set of title-class identifiers, thatassociate particular titles, of the standardized set of titles, andparticular role classifications that are part of a standardizedhierarchy of role classifications, as described above.

As further shown in FIG. 6 , process 600 may include obtaininginformation relating to particular standardized roles that is stored inassociation with the subset of title-class identifiers (block 650). Forexample, the role management platform (e.g., using computing resource235, processor 320, memory 330, storage component 340, input component350, communication interface 370, and/or the like) may obtaininformation relating to particular standardized roles that is stored inassociation with the subset of title-class identifiers, as describedabove.

As further shown in FIG. 6 , process 600 may include performing one ormore actions to cause one or more tasks associated with the set oforganization-specific roles to be modified or eliminated based on theparticular relating to the particular standardized roles (block 660).For example, the role management platform (e.g., using computingresource 235, processor 320, memory 330, storage component 340, outputcomponent 350, communication interface 370, and/or the like) may performone or more actions to cause one or more tasks associated with the setof organization-specific roles to be modified or eliminated based on theparticular relating to the particular standardized roles, as describedabove.

Process 600 may include additional implementations, such as any singleimplementation or any combination of implementations described belowand/or in connection with one or more other processes describedelsewhere herein.

In some implementations, the role management platform may set, afterreceiving the request, a configuration of the data model that is setbased on the information included in the request. In someimplementations, when setting the configuration of the data model, therole management platform may assign a first weighted value to a titleportion of a title-class identifier based on whether a title, of the setof titles, maps to a title of the particular titles included in thestandardized set of titles. Additionally, the role management platformmay assign a second weighted value to a first classification portion ofthe title-class identifier based on whether the title maps to a firstrole classification of the hierarchy of role classifications, and mayassign one or more additional weighted values to one or more otherclassification portions of the title-class identifier based on whetherthe title maps to one or more other role classifications of thehierarchy of role classifications. In some implementations, the one ormore additional weighted values may be greater than the first weightedvalue, and the one or more additional weighted values may be assignedbased on the hierarchy of role classifications.

In some implementations, the role management platform may convert thedata that identifies the set of titles to the set of vectors using aneural network.

In some implementations, the role management platform may, beforereceiving the request, receive data that identifies a first standardizedset of titles and a first hierarchy of role classifications. In someimplementations, the role management platform may receive data thatidentifies a first set of historical titles associated with a group oforganizations. In some implementations, the role management platform maygenerate data that identifies a second standardized set of titles byusing a system of adversarial neural networks to process the data thatidentifies the first standardized set of titles and the first hierarchyof role classifications. In some implementations, the role managementplatform may generate data that identifies a second set of historicaltitles by using the system of adversarial neural networks to process thedata that identifies the first set of historical titles. In someimplementations, the role management platform may generate data thatidentifies particular title-class identifiers by using the system ofadversarial neural networks to process the data that identifies thesecond standardized set of titles and the data that identifies thesecond set of historical titles. The particular title-class identifiersmay be included in the set of title-class identifiers.

In some implementations, when performing the one or more actions, therole management platform may generate a recommendation to modify oreliminate a task of an organization-specific role based on an analysisof particular information that describes a corresponding standardizedrole of the particular standardized roles. Additionally, the rolemanagement platform may determine that the recommendation satisfies athreshold confidence level. Additionally, the role management platformmay determine that the recommendation satisfies a threshold confidencelevel.

Although FIG. 6 shows example blocks of process 600, in someimplementations, process 600 may include additional blocks, fewerblocks, different blocks, or differently arranged blocks than thosedepicted in FIG. 6 . Additionally, or alternatively, two or more of theblocks of process 600 may be performed in parallel.

The foregoing disclosure provides illustration and description but isnot intended to be exhaustive or to limit the implementations to theprecise forms disclosed. Modifications and variations may be made inlight of the above disclosure or may be acquired from practice of theimplementations.

As used herein, the term “component” is intended to be broadly construedas hardware, firmware, and/or a combination of hardware and software.

Some implementations are described herein in connection with thresholds.As used herein, satisfying a threshold may, depending on the context,refer to a value being greater than the threshold, more than thethreshold, higher than the threshold, greater than or equal to thethreshold, less than the threshold, fewer than the threshold, lower thanthe threshold, less than or equal to the threshold, equal to thethreshold, or the like.

Certain user interfaces have been described herein and/or shown in thefigures. A user interface may include a graphical user interface, anon-graphical user interface, a text-based user interface, and/or thelike. A user interface may provide information for display. In someimplementations, a user may interact with the information, such as byproviding input via an input component of a device that provides theuser interface for display. In some implementations, a user interfacemay be configurable by a device and/or a user (e.g., a user may changethe size of the user interface, information provided via the userinterface, a position of information provided via the user interface,etc.). Additionally, or alternatively, a user interface may bepre-configured to a standard configuration, a specific configurationbased on a type of device on which the user interface is displayed,and/or a set of configurations based on capabilities and/orspecifications associated with a device on which the user interface isdisplayed.

It will be apparent that systems and/or methods described herein may beimplemented in different forms of hardware, firmware, or a combinationof hardware and software. The actual specialized control hardware orsoftware code used to implement these systems and/or methods is notlimiting of the implementations. Thus, the operation and behavior of thesystems and/or methods are described herein without reference tospecific software code—it being understood that software and hardwaremay be designed to implement the systems and/or methods based on thedescription herein.

Even though particular combinations of features are recited in theclaims and/or disclosed in the specification, these combinations are notintended to limit the disclosure of various implementations. In fact,many of these features may be combined in ways not specifically recitedin the claims and/or disclosed in the specification. Although eachdependent claim listed below may directly depend on only one claim, thedisclosure of various implementations includes each dependent claim incombination with every other claim in the claim set.

No element, act, or instruction used herein should be construed ascritical or essential unless explicitly described as such. Also, as usedherein, the articles “a” and “an” are intended to include one or moreitems and may be used interchangeably with “one or more.” Furthermore,as used herein, the term “set” is intended to include one or more items(e.g., related items, unrelated items, a combination of related andunrelated items, etc.), and may be used interchangeably with “one ormore.” Where only one item is intended, the phrase “only one” or similarlanguage is used. Also, as used herein, the terms “has,” “have,”“having,” or the like are intended to be open-ended terms. Further, thephrase “based on” is intended to mean “based, at least in part, on”unless explicitly stated otherwise.

What is claimed is:
 1. A method, comprising: receiving, by a device,data that identifies a first standardized set of titles and a firsthierarchy of role classifications; receiving, by the device, data thatidentifies a first set of historical titles associated with a group oforganizations; generating, by the device, data that identifies a secondstandardized set of titles by using a system of adversarial neuralnetworks to process the data that identifies the first standardized setof titles and the first hierarchy of role classifications; generating,by the device, data that identifies a second set of historical titles byusing the system of adversarial neural networks to process the data thatidentifies the first set of historical titles; generating, by thedevice, data that identifies particular title-class identifiers by usingthe system of adversarial neural networks to process the data thatidentifies the second standardized set of titles and the data thatidentifies the second set of historical titles; receiving, by thedevice, a request associated with standardizing a set oforganization-specific roles within an organization of the group oforganizations, wherein the request includes information comprising oneor more of: data that identifies a set of titles for the set oforganization-specific roles, or an industry identifier for an industryin which the organization operates; converting, by the device, the datathat identifies the set of titles to a set of vectors that representsemantic meanings of the set of titles; setting, by the device and basedon the information included in the request, a configuration of a datamodel that is capable of scoring the set of titles, wherein setting theconfiguration includes assigning weighted values to a set of title-classidentifiers, including the particular title-class identifiers, that areused, by a set of adversarial neural networks, including the system ofadversarial neural networks, of the data model, to associate astandardized set of titles to a hierarchy of role classifications thatidentify classes used to group standardized roles within organizations,and wherein the weighted values are assigned based on at least one of: aspecific hierarchy of role classifications within the organization, orthe industry in which the organization operates, and wherein the set ofadversarial neural networks is trained based on historical titlesassociated with the set of title-class identifiers; determining, by thedevice and by using the data model that has been configured with theweighted values to process the set of vectors, a set of scores thatindicate likelihoods of the set of titles mapping to the set oftitle-class identifiers; identifying, by the device and based on the setof scores, a subset of title-class identifiers, of the set oftitle-class identifiers, that associate particular titles, of thestandardized set of titles, and particular role classifications that arepart of the hierarchy of role classifications, wherein the subset oftitle-class identifiers is stored in association with informationrelating to particular standardized roles; providing, by the device andvia an application programming interface, the information relating tothe particular standardized roles to a client device by generating arecommendation to modify or eliminate one or more tasks associated withthe set of organization-specific roles; and performing, by the deviceand by using the information relating to the particular standardizedroles, one or more actions to cause one or more tasks associated withthe set of organization-specific roles to be modified or eliminated. 2.The method of claim 1, wherein setting the configuration of the datamodel comprises: assigning a first weighted value to a title portion ofa title-class identifier based on whether a title, of the set of titles,maps to a particular title, of the standardized set of titles, andassigning one or more additional weighted values to one or moreclassification portions of the title-class identifier based on whetherthe title maps to one or more role classifications of the hierarchy ofrole classifications.
 3. The method of claim 2, wherein the firstweighted value and the one or more additional weighted values are storedin a vector format.
 4. The method of claim 2, wherein the one or moreadditional weighted values are greater than the first weighted value;and wherein the one or more additional weighted values are assignedbased on the hierarchy of role classifications.
 5. The method of claim1, wherein the data model comprises a neural network.
 6. The method ofclaim 1, wherein performing the one or more actions comprises:generating another recommendation to modify or eliminate a task of anorganization-specific role based on an analysis of particularinformation that describes a corresponding standardized role of theparticular standardized roles; and providing the other recommendation toa user device to cause the user device to perform particular actions toimplement the other recommendation.
 7. A device, comprising: one or morememories; and one or more processors, operatively coupled to the one ormore memories, to: receive data that identifies a first standardized setof titles and a first hierarchy of role classifications; receive datathat identifies a first set of historical titles associated with a groupof organizations; generate data that identifies a second standardizedset of titles by using a system of adversarial neural networks toprocess the data that identifies the first standardized set of titlesand the first hierarchy of role classifications; generate data thatidentifies a second set of historical titles by using the system ofadversarial neural networks to process the data that identifies thefirst set of historical titles; generate data that identifies particulartitle-class identifiers by using the system of adversarial neuralnetworks to process the data that identifies the second standardized setof titles and the data that identifies the second set of historicaltitles; receive a data model that includes a set of generativeadversarial networks, including the system of adversarial neuralnetworks, that have been trained to map titles to: a standardized set oftitles for standardized roles within organizations that are identified,by the set of generative adversarial networks, based on historicaltitles associated with the organizations, and a hierarchy of roleclassifications that identify classes used to group the standardizedroles; receive a request associated with standardizing a set oforganization-specific roles within an organization of the group oforganizations, wherein the request includes information comprising datathat identifies a storage location for a set of titles for the set oforganization-specific roles; obtain data that identifies the set oftitles using the data that identifies the storage location; convert thedata that identifies the set of titles to a set of vectors thatrepresent semantic meanings of the set of titles; determine, by usingthe data model to process the set of vectors, a set of scores thatindicate likelihoods of the set of titles mapping to a set oftitle-class identifiers, including the particular title-classidentifiers, associated with the historical titles, wherein the set oftitle-class identifiers are to be used to associate the standardized setof titles to the hierarchy of role classifications that identify typesof the standardized roles within the organizations, and wherein the setof scores are to be determined using a cost function of the data modelthat assigns weighted values to the set of title-class identifiers basedon the information included in the request; identify, based on the setof scores, a subset of title-class identifiers, of the set oftitle-class identifiers, that associate particular titles, of thestandardized set of titles, and particular role classifications that arepart of a standardized hierarchy of role classifications; obtaininformation relating to particular standardized roles that is stored inassociation with the subset of title-class identifiers; provide theinformation relating to the particular standardized roles to a clientdevice by generating a recommendation to modify or eliminate one or moretasks associated with the set of organization-specific roles; andperform one or more actions to cause one or more tasks associated withthe set of organization-specific roles to be modified or eliminatedbased on the information relating to the particular standardized roles.8. The device of claim 7, wherein the request includes an industryidentifier of an industry in which the organization operates; andwherein the one or more processors are further to: set a configurationof the data model based on the information included in the request,wherein setting the configuration includes assigning the weighted valuesto the set of title-class identifiers based on the industry in which theorganization operates.
 9. The device of claim 7, wherein the one or moreprocessors are further to: set, after receiving the request, aconfiguration of the data model that is set based on the informationincluded in the request, wherein the one or more processors, whensetting the configuration of the data model, are to: assign a firstweighted value to a title portion of a title-class identifier based onwhether a title, of the set of titles, maps to a standardized title ofthe set of standardized titles, and assign one or more additionalweighted values to one or more classification portions of thetitle-class identifier based on whether the title maps to one or morejob classifications of the hierarchy of job classifications.
 10. Thedevice of claim 9, wherein the one or more additional weighted valuesare greater than the first weighted value, and wherein the one or moreadditional weighted values are assigned based on the hierarchy of roleclassifications.
 11. The device of claim 7, wherein the data model is aneural network; and wherein the one or more processors, when determiningthe set of scores, are to: provide the set of vectors as input to theneural network to cause the neural network to output the set of scores,wherein the cost function assigns each title-class identifier, of theset of title-class identifiers: a first weighted value to a titleportion of a title-class identifier based on whether a title, of the setof titles, maps to a title of the standardized set of titles, and one ormore additional weighted values to one or more classification portionsof the title-class identifier based on whether the title maps to one ormore role classifications of the hierarchy of role classifications. 12.The device of claim 7, wherein the one or more processors are furtherto: receive, from a user device, feedback information indicating whetherthe set of organization-specific roles map to the set of title-classidentifiers; and update the weighted values that are assigned to the setof title-class identifiers based on the feedback information.
 13. Thedevice of claim 7, wherein the one or more processors, when performingthe one or more actions, are to: generate another recommendation tomodify or eliminate a task of an organization-specific role based on ananalysis of particular information that describes a correspondingstandardized role of the particular standardized roles, wherein theother recommendation includes at least one of: data that identifies thetask or the organization-specific role that is to be modified oreliminated, data that identifies when to modify or to eliminate the taskor the organization-specific role, or data that identifies how to modifyor to eliminate the task or the organization-specific role; and providethe other recommendation to a user device to permit the user device todisplay the other recommendation via an interface.
 14. A non-transitorycomputer-readable medium storing instructions, the instructionscomprising: one or more instructions that, when executed by one or moreprocessors of a device, cause the one or more processors to: receivedata that identifies a first standardized set of titles and a firsthierarchy of role classifications; receive data that identifies a firstset of historical titles associated with a group of organizations;generate data that identifies a second standardized set of titles byusing a system of adversarial neural networks to process the data thatidentifies the first standardized set of titles and the first hierarchyof role classifications; generate data that identifies a second set ofhistorical titles by using the system of adversarial neural networks toprocess the data that identifies the first set of historical titles;generate data that identifies particular title-class identifiers byusing the system of adversarial neural networks to process the data thatidentifies the second standardized set of titles and the data thatidentifies the second set of historical titles; receive a requestassociated with standardizing a set of organization-specific roleswithin an organization of the group of organizations, wherein therequest includes information comprising: data that identifies a set oftitles for the set of organization-specific roles, and an industryidentifier of an industry in which the organization operates; convertthe data that identifies the set of titles to a set of vectors thatrepresent semantic meanings of the set of titles; determine, by using adata model to process the set of vectors, a set of scores that indicatelikelihoods of the set of titles mapping to a set of title-classidentifiers including the particular title-class identifiers, whereinthe set of title-class identifiers are to be used to associate astandardized set of titles to a hierarchy of role classifications thatidentify classes used to group standardized roles within organizations,and wherein the set of scores are to be determined using a cost functionof the data model that assigns weighted values to the set of title-classidentifiers based on the information included in the request; identify,based on the set of scores, a subset of title-class identifiers, of theset of title-class identifiers, that associate particular titles, of thestandardized set of titles, and particular role classifications that arepart of a standardized hierarchy of role classifications; obtaininformation relating to particular standardized roles that is stored inassociation with the subset of title-class identifiers; provide theinformation relating to the particular standardized roles to a clientdevice by generating a recommendation to modify or eliminate one or moretasks associated with the set of organization-specific roles; andperform one or more actions to cause one or more tasks associated withthe set of organization-specific roles to be modified or eliminatedbased on the information relating to the particular standardized roles.15. The non-transitory computer-readable medium of claim 14, wherein theone or more instructions, when executed by the one or more processors,further cause the one or more processors to: set, after receiving therequest, a configuration of the data model that is set based on theinformation included in the request, wherein the one or moreinstructions, that cause the one or more processors to set theconfiguration of the data model, cause the one or more processors to:assign a first weighted value to a title portion of a title-classidentifier based on whether a title, of the set of titles, maps to atitle of the particular titles included in the standardized set oftitles, assign a second weighted value to a first classification portionof the title-class identifier based on whether the title maps to a firstrole classification of the hierarchy of role classifications, and assignone or more additional weighted values to one or more otherclassification portions of the title-class identifier based on whetherthe title maps to one or more other role classifications of thehierarchy of role classifications.
 16. The non-transitorycomputer-readable medium of claim 15, wherein the one or more additionalweighted values are greater than the first weighted value, and whereinthe one or more additional weighted values are assigned based on thehierarchy of role classifications.
 17. The non-transitorycomputer-readable medium of claim 14, wherein the one or moreinstructions, that cause the one or more processors to convert the datathat identifies the set of data, cause the one or more processors to:convert the data that identifies the set of titles to the set of vectorsusing a neural network.
 18. The non-transitory computer-readable mediumof claim 14, wherein the one or more instructions, that cause the one ormore processors to perform the one or more actions, cause the one ormore processors to: generate another recommendation to modify oreliminate a task of an organization-specific role based on an analysisof particular information that describes a corresponding standardizedrole of the particular standardized roles; determine that the otherrecommendation satisfies a threshold confidence level; and automaticallyimplement the other recommendation based on the other recommendationsatisfying the threshold confidence level.
 19. The method of claim 1,wherein the system of adversarial neural networks includes one or moregenerator networks.
 20. The non-transitory computer-readable medium ofclaim 14, wherein the system of adversarial neural networks includes oneor more discriminator networks.